基于联邦卡尔曼滤波的GPS/TDOA混合定位算法

Cui-Xia Li, Wei-Ming Liu, Zi-Nan Fu
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引用次数: 12

摘要

一般来说,GPS(Global Positioning System,全球定位系统)的精度要高于蜂窝网络。但在高楼林立的城区或室内,GPS信号相对较弱,定位不稳定,定位不准确。TDOA(Time Difference of Arrival)是蜂窝移动通信系统中应用最广泛的定位方法之一,但其精度不足以满足日益增长的需求。在城市中,基站较多,需要支持较大的通信容量,因此移动终端的定位精度和稳定性相对较好。为了弥补GPS/TDOA单独定位的这些固有缺陷,本文通过给出各局部卡尔曼滤波器的误差模型、状态方程和测量方程,推导出一种基于联邦卡尔曼滤波器的GPS/TDOA混合定位算法。仿真结果表明,该算法有效地提高了数据融合的可靠性和定位精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GPS/TDOA Hybrid Location Algorithm Based on Federal Kalman Filter
Generally, accuracy of GPS(Global Positioning System) is higher than that of cellular network. But in tall building urban area or indoor, relatively weak GPS signal makes the positioning unstable and inaccurate. TDOA(Time Difference of Arrival) is one of the most widely used positioning methods in cellular mobile communication systems, but its accuracy is not high enough to meet the growing demand. In urban where there are more base stations to support big communication capacity than in rural, the mobile terminal positioning accuracy and stability is relatively better. In order to make up for these inherent deficiency of GPS or TDOA separate positioning, this paper deduces a GPS/TDOA hybrid positioning algorithm based on federated kalman filter by giving error model, state equation and measurement equation of each local kalman filters. Simulation results show that the algorithm effectively improves the data fusion reliability and positioning accuracy.
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